ArticleBioinformatics (Oxford, England)2020
Regional imaging genetic enrichment analysis.
Article in Bioinformatics (Oxford, England), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 2 of them syntheses that pooled it.
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Who cites it
17 citing papers in PubMed, 2 syntheses or guidelines pooled it, 22 citations in OpenAlex.
- The Intersection of Genetics and Neuroimaging: A Systematic Review of Imaging Genetics in Neurological Disease for Personalized Treatment.Journal of molecular neuroscience : MN · 2025Pooled it
- Integrative analysis of summary data from GWAS and eQTL studies implicates genes differentially expressed in Alzheimer's disease.BMC genomics · 2022Pooled it
- BriGHT: transcriptome-regularized multimodal neuroimaging for brain disorder prediction.Bioinformatics (Oxford, England) · 2026Article
- Testing for Network Specificity in Brain-Behavior Associations Using Ordinal Dominance Curves.Human brain mapping · 2026Article
- A Module-Level Polygenic Risk Score-Based NetWAS Framework for Identifying AD Genetic Modules Mediated by Amygdala: An ADNI Study.International journal of molecular sciences · 2025Article
- Genetic underpinnings of brain structural connectome for young adults.Journal of the American Statistical Association · 2023Article
- Discovering Precision AD Biomarkers with Varying Prognosis Effects in Genetics Driven Subpopulations.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2023Article
- Deep multiview learning to identify imaging-driven subtypes in mild cognitive impairment.BMC bioinformatics · 2022Article
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- Genome-Wide association study of quantitative biomarkers identifies a novel locus for alzheimer's disease at 12p12.1.BMC genomics · 2022Article
- GC-CNNnet: Diagnosis of Alzheimer's Disease with PET Images Using Genetic and Convolutional Neural Network.Computational intelligence and neuroscience · 2022Article
- Imaging Genetics in Epilepsy: Current Knowledge and New Perspectives.Frontiers in molecular neuroscience · 2022Review
- Hippocampal Subregion and Gene Detection in Alzheimer's Disease Based on Genetic Clustering Random Forest.Genes · 2021Article
- Genome-wide variant-based study of genetic effects with the largest neuroanatomic coverage.BMC bioinformatics · 2021Article
- Genetic Influence Underlying Brain Connectivity Phenotype: A Study on Two Age-Specific Cohorts.Frontiers in genetics · 2021Article
- Identifying imaging genetic associations via regional morphometricity estimation.Pacific Symposium on Biocomputing. Pacific Symposium on BiocomputingArticle
- Identifying highly heritable brain amyloid phenotypes through mining Alzheimer's imaging and sequencing biobank data.Pacific Symposium on Biocomputing. Pacific Symposium on BiocomputingArticle
Corrections and comments
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Authors and funding
9 authors at 3 institutions in 1 country.
Funding
Abstract
motivationBrain imaging genetics aims to reveal genetic effects on brain phenotypes, where most studies examine phenotypes defined on anatomical or functional regions of interest (ROIs) given their biologically meaningful interpretation and modest dimensionality compared with voxelwise approaches. Typical ROI-level measures used in these studies are summary statistics from voxelwise measures in the region, without making full use of individual voxel signals.
resultsIn this article, we propose a flexible and powerful framework for mining regional imaging genetic associations via voxelwise enrichment analysis, which embraces the collective effect of weak voxel-level signals and integrates brain anatomical annotation information. Our proposed method achieves three goals at the same time: (i) increase the statistical power by substantially reducing the burden of multiple comparison correction; (ii) employ brain annotation information to enable biologically meaningful interpretation and (iii) make full use of fine-grained voxelwise signals. We demonstrate our method on an imaging genetic analysis using data from the Alzheimer's Disease Neuroimaging Initiative, where we assess the collective regional genetic effects of voxelwise FDG-positron emission tomography measures between 116 ROIs and 565 373 single-nucleotide polymorphisms. Compared with traditional ROI-wise and voxelwise approaches, our method identified 2946 novel imaging genetic associations in addition to 33 ones overlapping with the two benchmark methods. In particular, two newly reported variants were further supported by transcriptome evidences from region-specific expression analysis. This demonstrates the promise of the proposed method as a flexible and powerful framework for exploring imaging genetic effects on the brain. AVAILABILITY AND IMPLEMENTATION: The R code and sample data are freely available at https://github.com/lshen/RIGEA. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.